This specification relates to approaches to organizing trained and untrained neural networks, and to methods of organizing of neural networks.
Neural networks are devices that are inspired by the structure and functional aspects of networks of biological neurons. In particular, neural networks mimic the information encoding and other processing capabilities of networks of biological neurons using a system of interconnected constructs called “nodes.” The arrangement and strength of connections between nodes in a neural network determines the results of information processing or information storage by a neural network.
Neural networks can be “trained” to produce a desired signal flow within the network and achieve desired information processing or information storage results. In general, training a neural network will change the arrangement and/or strength of connections between nodes during a learning phase. A neural network can be considered “trained” when sufficiently appropriate processing results are achieved by the neural network for given sets of inputs.
Neural networks can be used in a variety of different devices to perform non-linear data processing and analysis. Non-linear data processing does not satisfy the superposition principle, i.e., the variables that are to be determined cannot be written as a linear sum of independent components. Examples of contexts in which non-linear data processing is useful include pattern and sequence recognition, novelty detection and sequential decision making, complex system modeling, and systems and techniques in a variety of other contexts